Abstract. Nuclear medicine imaging modalities assist commonly in surgical guidance given their functional nature. However, when used in the operating room they present limitations. Pre-operative tomographic 3D imaging can only serve as a vague guidance intra-operatively, due to movement, deformation and changes in anatomy since the time of imaging, while standard intra-operative nuclear measurements are limited to 1D or (in some cases) 2D images with no depth information. To resolve this problem we propose the synchronized acquisition of position, orientation and readings of gamma probes intra-operatively to reconstruct a 3D activity volume. In contrast to conventional emission tomography, here, in a first proof-of-concept, the reconstruction succeeds without requiring symmetry in the positions and angles of acquisition, which allows greater flexibility. We present our results in phantom experiments for sentinel node lymph node localization. The results indicate that 3D intra-operative nuclear images can be generated in such a setup up to an accuracy equivalent to conventional SPECT systems. This technology has the potential to advance standard procedures towards intra-operative 3D nuclear imaging and offers a novel approach for robust and precise localization of functional information to facilitate less invasive, image-guided surgery.
Interest in poisoning attacks and backdoors recently resurfaced for Deep Learning (DL) applications. Several successful defense mechanisms have been recently proposed for Convolutional Neural Networks (CNNs), for example in the context of autonomous driving. We show that visualization approaches can aid in identifying a backdoor independent of the used classifier. Surprisingly, we find that common defense mechanisms fail utterly to remove backdoors in DL for Intrusion Detection Systems (IDSs). Finally, we devise pruning-based approaches to remove backdoors for Decision Trees (DTs) and Random Forests (RFs) and demonstrate their effectiveness for two different network security datasets.
Nuclear imaging modalities are commonly used tools in today's diagnostics and therapy planning. However for interventional use they suffer from drawbacks which limit their application. Freehand SPECT was developed to provide 3D functional imaging during interventions. It combines a nuclear detector with an optical tracking system to obtain its position and orientation in space and synchronizes this with the detector readings. This information can be used to compute a 3D tomographic reconstruction of an activity distribution of a nuclear tracer. As there is no fixed geometry, the system matrix has to be computed on the fly. This is done with models of the detection process for completely arbitrary freehand acquisitions. The accuracy of the reconstructions is highly dependent on the used models of the detection process. Different models of the detection process were developed and evaluated in this work, in particular two analytical models as well as lookup tables generated from either real measurements or Monte Carlo simulations. We showed that it is possible to perform acceptable reconstructions with a simple but efficient analytical model. The use of lookup tables to generate the system matrix in Freehand SPECT is a fast solution with good accuracy.
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